Fetal

Atherosclerotic plaques, which are fatty build-ups in arteries, can develop silently and may rupture, leading to serious cardiovascular events like heart attacks or strokes. Despite treatments and controlling risks such as diabetes and high blood pressure, some people still develop dangerous forms of the disease. Researchers have found that plaques with thin caps, soft centers, or tiny new blood vessels are more likely to rupture, so new imaging methods are being developed to detect these vulnerable plaques earlier. This study evaluates a new 3D ultrasound probe and machine that can capture many more images than standard probes to visualize the entire plaque and its tiny blood vessels more accurately. Patients will receive an infusion of a microbubble contrast agent called SonoVue to enhance imaging. Initial 2D ultrasound images will confirm plaque presence, followed by 3D imaging using a specialized row-column array probe manipulated by a clinician. The 3D scan takes about 2 to 5 minutes and captures multiple images to assess plaque neovascularization. Participants will undergo ultrasound scans of their carotid arteries after receiving the contrast agent via an infusion. Researchers will analyze these images offline to detect new blood vessels within plaques. The main measurement is the detection of carotid plaque neovascularization at baseline. The study begins with consenting patients who have carotid plaques confirmed by previous CEUS scans. Participation involves imaging procedures and consent, with the study lasting as long as these assessments are completed.

Researchers are investigating early detection and risk assessment of hepatocellular carcinoma HCC in adults with advanced liver disease, specifically those with liver cirrhosis from various causes. This prospective multicenter study aims to compare ultrasound and abbreviated MRI AMRI as surveillance tools to assess their ability to detect HCC, as well as to study how body composition, such as fat and muscle levels, relates to disease progression and HCC risk. Participants with cirrhosis undergo regular clinical evaluations and imaging tests at set intervals, including ultrasound and abbreviated MRI scans. The study collects detailed data on body composition and tracks clinical outcomes like liver-related complications and mortality. These assessments occur over multiple visits, including baseline and follow-ups at 6, 12, and 18 months, with additional monitoring for up to 24 months to observe new cases of HCC and disease progression. During the study, participants receive structured exams, imaging, and body composition measurements at each visit. Researchers evaluate lesion risk for HCC using LI-RADS criteria and measure muscle mass through the Muscle Assessment Score. They also monitor liver stiffness and organ volumes at baseline and follow-up visits. The studys goal is to develop prediction models based on these clinical and imaging data to better understand HCC risk and liver disease progression over time.

Researchers are evaluating a new deep learning model that uses ultrasound video data to estimate blood volume before surgery. This prospective, single-center observational study focuses on adult patients scheduled for surgery, aiming to provide an accurate, non-invasive way to assess blood volume, which is important to prevent complications during surgery. The study is led by Shanghai 6th Peoples Hospital and addresses the current lack of practical methods for direct blood volume measurement. Participants will undergo preoperative ultrasound scans capturing videos of four major blood vessels the Internal Jugular Vein, Subclavian Vein, Inferior Vena Cava, and Common Carotid Artery. The true blood volume is calculated using a clinical method involving hemoglobin concentration changes before and after acute normovolemic hemodilution. The collected ultrasound videos will be used to train and validate a deep learning model that combines convolutional and temporal analysis techniques to estimate blood volume from the ultrasound data. During the study, participants will have ultrasound video clips taken shortly before surgery. Researchers will analyze these images alongside blood volume values determined by the clinical method. The main outcome measured is the accuracy of the blood volume estimate within 30 minutes before surgery. The study does not involve treatment changes but focuses on data collection and model validation. Participation lasts through the preoperative period up to surgery, with no additional follow-up specified.

Researchers are conducting a Phase 3 clinical trial to evaluate the safety and effectiveness of AOC 1044, also known as delpacibart zotadirsen, for treating Duchenne Muscular Dystrophy DMD in boys aged 7 to 16 with specific gene mutations suitable for exon 44 skipping. This study is designed as a randomized, double-blind, placebo-controlled trial to assess the impact of this intravenous treatment on muscle function over time. Participants will be randomly assigned to receive either AOC 1044 or a placebo infusion every 6 weeks for 54 weeks, totaling 9 doses during the double-blind treatment period. After this, all participants can join an open-label extension where they receive AOC 1044 every 6 weeks for another 54 weeks, adding 9 more doses. Following the final dose at week 102, participants will have assessments at weeks 108 and 114 to evaluate safety and treatment effects. During the study, participants will undergo various assessments including tests for time to rise velocity, muscle strength, walking and climbing abilities, and quality of life measures. Muscle enzyme levels and global impressions of severity and change from both patients and caregivers will also be monitored. Safety and tolerability will be reviewed regularly by an independent committee. Overall participation lasts over two years, including screening, treatment, extension, and follow-up phases.

Researchers are studying the use of two new surgical devices, the Karl Storz Curved and Straight Fetoscopes, to assist in minimally invasive in-utero surgeries. These devices help surgeons see inside the uterus during procedures to treat fetal conditions like twin-twin transfusion syndrome and other complex anomalies. The study aims to assess surgical outcomes, complications, and gestational age in patients treated with these scopes, helping to understand their potential benefits in fetal surgery. The curved fetoscope is designed for patients with an anterior placenta, while the straight fetoscope is used for those with a posterior placenta. Both scopes assist in fetoscopic laser photocoagulation, a procedure that seals abnormal blood vessels in the placenta using laser energy. This research is a non-randomized, single arm study where eligible patients undergo intrauterine procedures with either the curved or straight scopes based on placenta position. The study will collect data on the success of the procedures and related complications for descriptive analysis. Participants will be monitored through the procedure and follow-up until birth and shortly after. The study will evaluate outcomes such as the rate of completed fetoscopic procedures, fetal survival at birth, and various maternal and fetal complications. Researchers will also track gestational age at delivery, procedure length, and short-term neonatal health. The total study duration extends over several years to capture both immediate and longer-term results of using these devices in fetal surgery.

Researchers are investigating whether longer admission to the Kangaroo Mother Care KMC ward after discharge from the neonatal intensive care unit NICU reduces postpartum depression in mothers of low birthweight preterm infants in low-resource settings. This randomized controlled trial compares extended KMC admission of more than 2 days to standard care of less than 2 days. The study also explores the impact on infant neurodevelopment, paternal depression, barriers to KMC practice, and cost effectiveness. Participants in the trial are randomly assigned to either stay on the KMC ward for more than 2 days or less than 2 days following NICU discharge. Mothers and infants practice kangaroo care, breastfeeding, and infant care during their stay. The study involves follow-up visits at 2 weeks, 6-8 weeks, and at 6, 12, and 18 months, to assess maternal depression, paternal depression, infant development, and barriers to KMC both in the hospital and after discharge. Mothers and fathers will be screened for depression at multiple time points while infants undergo neurodevelopmental assessments up to 18 months of age. Researchers will track hours spent practicing KMC, breastfeeding duration, infant growth measurements, morbidity, mortality, and healthcare costs. Data collection includes questionnaires on social support and interviews to identify KMC barriers. The study aims to provide a comprehensive view of maternal, paternal, and infant outcomes related to KMC duration in a low-resource setting.

This research aims to investigate whether artificial intelligence AI can detect imaging features typical of Intensive Care Unit-acquired Weakness ICUAW using neuromuscular ultrasound. The study focuses on evaluating if AI-based image analysis can identify and monitor ICU patients with ICUAW and whether these AI results correlate with muscle weakness severity, visual muscle echogenicity grading, and 30- and 90-day patient outcomes. ICUAW is a common neuromuscular complication in critically ill patients, often difficult to assess due to patient sedation and limited cooperation during clinical exams. Participants will undergo non-invasive neuromuscular ultrasound of peripheral muscles in the upper and lower limbs. The ultrasound images will be processed using AI, specifically Convolutional Neural Networks, to classify muscle weakness severity. Explainable AI techniques will also be used to highlight the areas within the ultrasound images that contribute to the AIs decisions, helping to understand muscle changes. The study includes groups of critically ill patients with and without ICUAW as well as healthy controls. During the study, researchers will assess muscle echogenicity abnormalities by ultrasound on Day 14 and measure ICUAW severity through various scales. Additional outcomes such as ventilation duration, hospital stay length, survival, frailty, and overall recovery will be evaluated at 30 and 90 days. Data collection involves clinical examinations, scoring systems, and AI image analysis to improve diagnosis and monitoring of muscle weakness in ICU patients.

Researchers are evaluating the accuracy and feasibility of an AI-assisted blind-sweep obstetric ultrasound called SPAQ E-con AI, used by trained non-specialist health workers for antenatal screening in rural Democratic Republic of the Congo. This multi-center prospective study includes about 1,430 pregnant women and aims to compare AI measurements of gestational age and fetal conditions to manual measurements by expert readers. The study also includes an adaptive model update plan following regulatory guidance. Participants undergo ultrasound scans using a smartphone-based 9-sweep technique operated by trained non-specialists after short training. The AI estimates gestational age, fetal presentation, and placenta location. The study includes natural enrollment of antenatal care attendees and enriched groups for placenta praevia, fetal malpresentation, and early pregnancy cases. Data collection spans up to 12 months, with interim analyses and adaptive updates planned. During the study, participants receive routine antenatal care while undergoing ultrasound screening. Researchers measure gestational age accuracy in second and third trimesters, AI confidence calibration, and secondary outcomes like sensitivity for placenta praevia, inter-rater reliability, and acceptability of the intervention. They also track protocol adherence, screening coverage, and sustainability of AI use. The study lasts through completion up to June 2027, with ongoing data collection and monitoring.

Researchers are exploring the relationship between ultrasound features, including conventional ultrasound, elastography, and contrast-enhanced ultrasound CEUS, and pathological prognostic factors in breast cancer. This observational study uses radiomics, a method that extracts detailed imaging information, to better understand these correlations and their potential clinical relevance. The study is sponsored by the Second Affiliated Hospital, School of Medicine, Zhejiang University, and aims to provide insights into breast cancer prognosis. Participants undergo ultrasound imaging assessments, including advanced techniques like elastography and CEUS, alongside standard pathological evaluations. The study observes patients over an average follow-up period of one year to monitor outcomes such as metastasis and death related to breast cancer progression. While endocrine therapy is noted as an intervention, the study primarily focuses on imaging and pathological data rather than treatment comparison. During the study, participants ultrasound characteristics and pathological findings are recorded and analyzed using radiomics technology. Researchers track disease-free survival and non-disease-free survival times, noting the time to relapse, metastasis, or death due to disease progression. Data collection includes follow-up results and pathology reports to ensure comprehensive monitoring. The total participation duration aligns with the one-year average follow-up to assess primary outcomes accurately.

Sarcopenia is an age-related condition involving loss of muscle mass and function, often occurring alongside diseases like cancer, diabetes, and chronic kidney disease, which worsen muscle decline and overall prognosis. This research aims to develop an artificial intelligence AI system using ultrasound images to diagnose sarcopenia and estimate patient strength, helping to identify individuals at risk early and improve quality of life for older adults. The study focuses on using ultrasound, a cost-effective and accessible imaging technique, to gather multidimensional data including muscle thickness and original image content, combined with basic information like age and body mass index. Researchers plan to create AI models for diagnosis, classification, and risk grading of sarcopenia in hospitalized and community-dwelling older adults. This approach aims to provide rapid screening for potential sarcopenia cases for further clinical management. Participants will undergo ultrasound scans to collect muscle images and measurements. Researchers will track outcomes such as death and diagnosed sarcopenia within two years after the initial ultrasound. The study involves older adults over 50 years old, including those at risk of sarcopenia based on walking or strength difficulties. Data collected will help build AI tools for early detection and risk assessment, with ongoing monitoring for up to two years.